LINGUIST List 7.73

Wed Jan 17 1996

Calls: Detecting and Preventing Miscommunication

Editor for this issue: Ann Dizdar <dizdartam2000.tamu.edu>

Directory

1. "Dr. Susan McRoy", CFP: Detecting and Preventing Miscommunication

DETECTING, REPAIRING, AND PREVENTING HUMAN--MACHINE MISCOMMUNICATION
 AAAI '96 Workshop---Portland, OR

Any system that communicates must be able to cope with the possibility
of miscommunication---including misunderstanding, non-understanding,
and misinterpretation:

 o In misunderstanding, one participant obtains an interpretation 
 that she believes is complete and correct, but which is,
 however, not the one that the other speaker intended her to
 obtain.

 o In non-understanding, a participant either fails to obtain any
 interpretation at all, or obtains more than one interpretation,
 with no way to choose among them.

 o In misinterpretation, the most likely interpretation of a 
 participant's utterance suggests that their beliefs about the
 world are unexpectedly out of alignment with the other's.

All three forms of miscommunication can eventually lead to repair in a
dialogue; however, misinterpretations and non-understandings are
typically recognized immediately, whereas a participant is not aware,
at least initially, when a misunderstanding occurs. Additionally,
misinterpretation can be a source of misunderstanding.

Successful communication requires that participants share considerable
knowledge. For example, they must share some knowledge about the
state of their interaction and about the physical and social situation
in which they are communicating. Knowledge of their interaction
includes the current topic under discussion (often a shared task), the
focus of attention, and the relevance of each utterance to the
previous interaction. In practice, no two participants start with an
identical understanding of their task or of the situation---nor can
they take the time to identify and resolve discrepancies beforehand.
As a result, participants must be prepared to handle miscommunication
during dialogue.

Research related to achieving robust interaction is an important
subarea in Artificial Intelligence (AI). Early work concerned the
correction of spelling or grammatical errors in a user's utterance so
that the system could more easily match them against a fixed
linguistic model; work has also been done in the area of speech
recognition, attempting to find the best fit of a sound signal to
legal sequences of linguistic objects. Other systems have attempted
to detect misconceptions in the user's model of the domain of
discourse. All of these approaches have assumed that the system's
model is always correct. More recently, researchers have been looking
at detecting and correcting errors in the system's model of an
interaction. This work includes research on speech repairs,
miscommunication, misunderstanding, non-understanding, and related
work in planning, such as plan misrecognition and plan repair.

The focus of this workshop is to bring together researchers interested
in developing theoretical models of robust interaction or in designing
robust systems. Topics of interest include, but are not limited to,
the following:

 o Theories that delineate what knowledge must be represented, how 
 it will be obtained and updated, and how responsibility for
 achieving robustness might be distributed among the interactants.

 o Strategies for identifying POTENTIAL causes of breakdowns, such as 
 ambiguities, misconceptions, and plan misrecognition, in order
 to avert miscommunication.

 o Strategies for identifying symptoms of ACTUAL breakdowns, such as 
 deviations from expected behavior, unresolvable ambiguities,
 and speech errors.

 o Techniques for correcting errors in interpretation that have 
 been used in other areas of AI, such as plan recognition and
 computer vision, and in related areas, such as human-computer 
 interaction and multimedia.

 o Approaches to minimizing and correcting miscommunication in 
 tutoring systems and education.

 o Empirical data regarding the occurrence of miscommunication and
 approaches to robust communication that derive from empirical
 methods.

 o Research in knowledge representation that would be useful
 in detecting, repairing, and preventing miscommunication.

We solicit papers that explore these issues, and papers that discuss
implementations of solutions to the problems of detecting, repairing,
and preventing human--machine miscommunication. Papers submitted to
the workshop should address these topics explicitly. As AAAI
procedures require, participation will be limited to 65.


COMMITTEE:
 Susan McRoy, chair
 University of Wisconsin--Milwaukee
 mcroycs.uwm.edu
 (414) 229--6695 (phone)
 (414) 229--6958 (fax)

 Brad Goodman Kathleen McCoy
 Mitre Corporation University of Delaware
 bgoodmanlinus.mitre.org mccoylouie.udel.edu

 Susan Haller Ronnie Smith
 University of Wisconsin--Parkside East Carolina University
 hallercs.uwp.edu rwsmath1.math.ecu.edu

 Graeme Hirst David Traum
 University of Toronto TECFA, Universite de Geneve
 ghcs.toronto.edu David.Traumtecfa.unige.ch


SCHEDULE:
 Submission deadline: March 18, 1996
 Author notification: April 15, 1996
 Camera-ready copy due: May 13, 1996
 Conference dates: August 4--8, 1996

SUBMISSIONS:
 Submit an extended abstract. Abstracts should not exceed 10 pages,
 exclusive of references, in 12 point, double-spaced text, with
 one-inch margins. 

 We strongly encourage electronic submissions, either plain text or 
 postscript. Emailed submissions should be emailed to
 mcroycs.uwm.edu with a subject heading ``ATTN: AAAI MNM''.
 In the event that electronic submission is not possible, send 6 
 copies to:

 Susan McRoy
 ATTN: AAAI MNM Workshop
 Computer Science, University of Wisconsin--Milwaukee
 3200 North Cramer Street, EMS Room 503
 Milwaukee, WI 53211

This cfp is on the WWW at http://www.cs.uwm.edu/faculty/mcroy/mnm.html